Skip to main content

2022 | OriginalPaper | Buchkapitel

Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection

verfasst von : Nirmalya Thakur, Chia Y. Han

Erschienen in: Human Interaction, Emerging Technologies and Future Systems V

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The scientific contribution of this paper is a Big Data-centric study, conducted using Google Trends, that involved analysis of the global, country-level, and state-level search trends related to indoor localization by mining relevant Google Search data from 2015–2020. There are three novel findings of this study. First, the current global search interest in indoor localization is higher than the average, median, and mode values of search interests (since 2015), and it is projected to keep increasing in the near future. Second, Singapore predominantly leads all other countries in terms of user interests in indoor localization. It is followed by Canada and United States, which are followed by the other countries. Third, the state-level analysis for the United States shows that Massachusetts leads all other states in terms of user interests in indoor localization. It is followed by New Jersey and Michigan, which are followed by the other states.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Langlois, C., Tiku, S., Pasricha, S.: Indoor localization with smartphones: harnessing the sensor suite in your pocket. IEEE Consum. Electron. Mag. 6(4), 70–80 (2017)CrossRef Langlois, C., Tiku, S., Pasricha, S.: Indoor localization with smartphones: harnessing the sensor suite in your pocket. IEEE Consum. Electron. Mag. 6(4), 70–80 (2017)CrossRef
2.
Zurück zum Zitat Gorecky, D., Schmitt, M., Loskyll, M., Zuhlke, D.: Human-machine-interaction in the industry 4.0 era. In: 2014 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289–294. IEEE (2014) Gorecky, D., Schmitt, M., Loskyll, M., Zuhlke, D.: Human-machine-interaction in the industry 4.0 era. In: 2014 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289–294. IEEE (2014)
3.
Zurück zum Zitat Thakur, N., Han, C.Y.: Multimodal approaches for indoor localization for ambient assisted living in smart homes. Information (Basel). 12(3), 114 (2021)CrossRef Thakur, N., Han, C.Y.: Multimodal approaches for indoor localization for ambient assisted living in smart homes. Information (Basel). 12(3), 114 (2021)CrossRef
4.
Zurück zum Zitat Dardari, D., Closas, P., Djuric, P.M.: Indoor tracking: theory, methods, and technologies. IEEE Trans Veh Technol. 64(4), 1263–1278 (2015)CrossRef Dardari, D., Closas, P., Djuric, P.M.: Indoor tracking: theory, methods, and technologies. IEEE Trans Veh Technol. 64(4), 1263–1278 (2015)CrossRef
6.
Zurück zum Zitat Preis, T., Moat, H.S., Stanley, H.E., Bishop, S.R.: Quantifying the advantage of looking forward. Sci. Rep. 2(1), 350 (2012)CrossRef Preis, T., Moat, H.S., Stanley, H.E., Bishop, S.R.: Quantifying the advantage of looking forward. Sci. Rep. 2(1), 350 (2012)CrossRef
7.
Zurück zum Zitat Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using Google Trends. Sci Rep. 3(1), 1684 (2013)CrossRef Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using Google Trends. Sci Rep. 3(1), 1684 (2013)CrossRef
8.
Zurück zum Zitat Mavragani, A., Ochoa, G., Tsagarakis, K.P.: Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J. Med. Internet Res. 20(11), e270 (2018) Mavragani, A., Ochoa, G., Tsagarakis, K.P.: Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J. Med. Internet Res. 20(11), e270 (2018)
9.
Zurück zum Zitat Chen, Y., Xie, J.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manage. Sci. 54(3), 477–491 (2008)CrossRef Chen, Y., Xie, J.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manage. Sci. 54(3), 477–491 (2008)CrossRef
11.
Zurück zum Zitat Mellon, J.: Where and when can we use Google Trends to measure issue salience? PS Polit Sci Polit. 46(02), 280–290 (2013)CrossRef Mellon, J.: Where and when can we use Google Trends to measure issue salience? PS Polit Sci Polit. 46(02), 280–290 (2013)CrossRef
12.
Zurück zum Zitat Hu, J., Liu, D., Yan, Z., Liu, H.: Experimental analysis on weight K-nearest neighbor indoor fingerprint positioning. IEEE Internet Things J. 6(1), 891–897 (2019)CrossRef Hu, J., Liu, D., Yan, Z., Liu, H.: Experimental analysis on weight K-nearest neighbor indoor fingerprint positioning. IEEE Internet Things J. 6(1), 891–897 (2019)CrossRef
13.
Zurück zum Zitat Qin, F., Zuo, T., Wang, X.: CCpos: WiFi fingerprint indoor positioning system based on CDAE-CNN. Sensors (Basel). 21(4), 1114 (2021)CrossRef Qin, F., Zuo, T., Wang, X.: CCpos: WiFi fingerprint indoor positioning system based on CDAE-CNN. Sensors (Basel). 21(4), 1114 (2021)CrossRef
14.
Zurück zum Zitat Ullah Khan, I., et al.: An improved hybrid indoor positioning system based on surface tessellation artificial neural network. Meas. Control. 53(9–10), 1968–1977 (2020) Ullah Khan, I., et al.: An improved hybrid indoor positioning system based on surface tessellation artificial neural network. Meas. Control. 53(9–10), 1968–1977 (2020)
16.
Zurück zum Zitat Zhang, L., Zhao, C., Wang, Y., Dai, L.: Fingerprint-based indoor localization using weighted K-nearest neighbor and weighted signal intensity. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture, New York, NY, USA. ACM (2020) Zhang, L., Zhao, C., Wang, Y., Dai, L.: Fingerprint-based indoor localization using weighted K-nearest neighbor and weighted signal intensity. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture, New York, NY, USA. ACM (2020)
17.
Zurück zum Zitat Gao, J., Li, X., Ding, Y., Su, Q., Liu, Z.: WiFi-based indoor positioning by random forest and adjusted cosine similarity. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 1426–1431. IEEE (2020) Gao, J., Li, X., Ding, Y., Su, Q., Liu, Z.: WiFi-based indoor positioning by random forest and adjusted cosine similarity. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 1426–1431. IEEE (2020)
18.
Zurück zum Zitat Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2006. New York, New York, USA. ACM Press (2006) Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2006. New York, New York, USA. ACM Press (2006)
Metadaten
Titel
Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection
verfasst von
Nirmalya Thakur
Chia Y. Han
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-85540-6_73